Quantifying Disparities in Air Pollution Exposures across the United States Using Home and Work Addresses.

While human mobility plays a crucial role in determining ambient air pollution exposures and health risks, research to date has assessed risks on the basis of almost solely residential location. Here, we leveraged a database of ∼128-144 million workers in the United States and published ambient PM2.5 data between 2011 and 2018 to explore how incorporating information on both workplace and residential location changes our understanding of disparities in air pollution exposure. In general, we observed higher workplace exposures relative to home exposures, as well as increased exposures for nonwhite and less educated workers relative to the national average. Workplace exposure disparities were higher among racial and ethnic groups and job types than by income, education, age, and sex. Not considering workplace exposures can lead to systematic underestimations in disparities in exposure among these subpopulations. We also quantified the error in assigning workers home instead of a weighted home-and-work exposure. We observed that biases in associations between PM2.5 and health impacts by using home instead of home-and-work exposure were the highest among urban, younger populations.

Evaluating the Performance of Low-Cost PM2.5 Sensors in Mobile Settings.

Low-cost sensors (LCSs) for measuring air pollution are increasingly being deployed in mobile applications, but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and those of higher-quality instruments? Can data from LCSs be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions, we deployed low-cost PM2.5 sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM2.5 reference monitors (Teledyne T640) at nearby regulatory sites. Next, using the reference measurements, we developed different models to correct the OPC-N3 and DustTrak measurements and then transferred the corrections to the mobile setting. We observed that more complex correction models appeared to perform better than simpler models in the stationary setting; however, when transferred to the mobile setting, corrected OPC-N3 measurements agreed less well with the corrected DustTrak data. In general, corrections developed by using minute-level collocation measurements transferred better to the mobile setting than corrections developed using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation relative to the direction of travel, date, hour-of-the-day, and road class together explain a small but significant amount of variation between corrected OPC-N3 and DustTrak measurements during the mobile deployment. Persistent hotspots identified by the OPC-N3s agreed with those identified by the DustTrak. Similarly, maps of PM2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements agreed well. These results suggest that identifying hotspots and developing generalizable maps of PM2.5 are appropriate use-cases for mobile LCS data.

Substantial Near-Field Air Quality Improvements at a General Aviation Airport Following a Runway Shortening.

Santa Monica Airport (SMO), a general aviation airport in Southern California, recently shortened its only runway by 225 m at both ends to limit jet aircraft operations. We evaluated the resulting changes in aviation activity and air quality by measuring particle number (PN), black carbon (BC), and lead (Pb) concentrations, before and after the runway was shortened at two near-airfield locations including a residential site. Postshortening, there was a 50% decrease in total operations, driven mostly by the greater than 80% decrease in jet operations; however, there was no significant change in piston engine aircraft operations (which use leaded fuel). We measured greater than 75%, 30%, and 75% reductions in the concentrations of PN, BC, and Pb, respectively, after the runway was shortened, largely due to enhanced dispersion resulting from the increased distance to the newly shortened runway. Overall, the runway shortening improved air quality in nearby areas such that airport impacts were comparable to or lower than impacts from other sources such as vehicular traffic. Until aviation fuel becomes completely unleaded, runway shortening or relocating operations away from the edge abutting residential areas may be the most effective environmental impact mitigation strategy for general aviation airports situated adjacent to residential areas.

Prenatal Ambient Ultrafine Particle Exposure and Childhood Asthma in the Northeastern United States.

Rationale: Ambient ultrafine particles (UFPs; with an aerodynamic diameter < 0.1 µm) may exert greater toxicity than other pollution components because of their enhanced oxidative capacity and ability to translocate systemically. Studies examining associations between prenatal UFP exposure and childhood asthma remain sparse. Objectives: We used daily UFP exposure estimates to identify windows of susceptibility of prenatal UFP exposure related to asthma in children, accounting for sex-specific effects. Methods: Analyses included 376 mother-child dyads followed since pregnancy. Daily UFP exposure during pregnancy was estimated by using a spatiotemporally resolved particle number concentration prediction model. Bayesian distributed lag interaction models were used to identify windows of susceptibility for UFP exposure and examine whether effect estimates varied by sex. Incident asthma was determined at the first report of asthma (3.6 ± 3.2 yr). Covariates included maternal age, education, race, and obesity; child sex; nitrogen dioxide (NO2) and temperature averaged over gestation; and postnatal UFP exposure. Measurements and Main Results: Women were 37.8% Black and 43.9% Hispanic, with 52.9% reporting having an education at the high school level or lower; 18.4% of children developed asthma. The cumulative odds ratio (95% confidence interval) for incident asthma per doubling of the UFP exposure concentration across pregnancy was 4.28 (1.41-15.7), impacting males and females similarly. Bayesian distributed lag interaction models indicated sex differences in the windows of susceptibility, with the highest risk of asthma seen in females exposed to higher UFP concentrations during late pregnancy. Conclusions: Prenatal UFP exposure was associated with asthma development in children, independent of correlated ambient NO2 and temperature. Findings will benefit future research and policy-makers who are considering appropriate regulations to reduce the adverse effects of UFPs on child respiratory health.

Impacts of Aviation Emissions on Near-Airport Residential Air Quality.

Impacts of aviation emissions on air quality in and around residences near airports remain underexamined. We measured gases (CO, CO2, NO, and NO2) and particles (black carbon, particle-bound aromatic hydrocarbons, fine particulate matter (PM2.5), and ultrafine particles (reported using particle number concentrations (PNC) as a proxy)) continuously for 1 month at a residence near the Logan International Airport, Boston. The residence was located under a flight trajectory of the most utilized runway configuration. We found that when the residence was downwind of the airport, the concentrations of all gaseous and particulate pollutants (except PM2.5) were 1.1- to 4.8-fold higher than when the residence was not downwind of the airport. Controlling for runway usage and meteorology, the impacts were highest during overhead landing operations: average PNC was 7.5-fold higher from overhead landings versus takeoffs on the closest runway. Infiltration of aviation-origin emissions resulted in indoor PNC that were comparable to ambient concentrations measured locally on roadways and near highways. In addition, ambient NO2 concentrations at the residence exceeded those measured at regulatory monitoring sites in the area including near-road monitors. Our results highlight the need for further characterization of outdoor and indoor impacts of aviation emissions at the neighborhood scale to more accurately estimate residential exposures.

Ultrafine Particle Number Concentration Model for Estimating Retrospective and Prospective Long-Term Ambient Exposures in Urban Neighborhoods.

Short-term exposure to ultrafine particles (UFP; <100 nm in diameter), which are present at high concentrations near busy roadways, is associated with markers of cardiovascular and respiratory disease risk. To date, few long-term studies (months to years) have been conducted due to the challenges of long-term exposure assignment. To address this, we modified hybrid land-use regression models of particle number concentrations (PNCs; a proxy for UFP) for two study areas in Boston (MA) by replacing the measured PNC term with an hourly model and adjusting for overprediction. The hourly PNC models used covariates for meteorology, traffic, and sulfur dioxide concentrations (a marker of secondary particle formation). We compared model performance against long-term PNC data collected continuously from 9 years before and up to 3 years after the model-development period. Model predictions captured the major temporal variations in the data and model performance remained relatively stable retrospectively and prospectively. The Pearson correlation of modeled versus measured hourly log-transformed PNC at a long-term monitoring site for 9 years prior was 0.74. Our results demonstrate that highly resolved spatial-temporal PNC models are capable of estimating ambient concentrations retrospectively and prospectively with generally good accuracy, giving us confidence in using these models in epidemiological studies.

Traffic-related particulate matter affects behavior, inflammation, and neural integrity in a developmental rodent model.

Recent studies indicate that exposure to airborne particulate matter (PM) is associated with cognitive delay, depression, anxiety, autism, and neurodegenerative diseases; however, the role of PM in the etiology of these outcomes is not well-understood. Therefore, there is a need for controlled animal studies to better elucidate the causes and mechanisms by which PM impacts these health outcomes. We assessed the effects of gestational and early life exposure to traffic-related PM on social- and anxiety-related behaviors, cognition, inflammatory markers, and neural integrity in juvenile male rats. Gestating and lactating rats were exposed to PM from a Boston (MA, USA) traffic tunnel for 5 h/day, 5 days/week for 6 weeks (3 weeks gestation, 3 weeks lactation). The target exposure concentration for the fine fraction of nebulized PM, measured as PM2.5, was 200 µg/m3. To assess anxiety and cognitive function, F1 male juveniles underwent elevated platform, cricket predation, nest building, social behavior and marble burying tests at 32-60 days of age. Upon completion of behavioral testing, multiple cytokines and growth factors were measured in these animals and their brains were analyzed with diffusion tensor MRI to assess neural integrity. PM exposure had no effect on litter size or weight, or offspring growth; however, F1 litters developmentally exposed to PM exhibited significantly increased anxiety (p = 0.04), decreased cognition reflected in poorer nest-organization (p = 0.04), and decreased social play and allogrooming (p = 0.003). MRI analysis of ex vivo brains revealed decreased structural integrity of neural tissues in the anterior cingulate and hippocampus in F1 juveniles exposed to PM (p < 0.01, p = 0.03, respectively). F1 juvenile males exposed to PM also exhibited significantly decreased plasma levels of both IL-18 (p = 0.03) and VEGF (p = 0.04), and these changes were inversely correlated with anxiety-related behavior. Chronic exposure of rat dams and their offspring to traffic-related PM during gestation and lactation decreases social behavior, increases anxiety, impairs cognition, decreases levels of inflammatory and growth factors (which are correlated with behavioral changes), and disrupts neural integrity in the juvenile male offspring. Our findings add evidence that exposure to traffic-related air pollution during gestation and lactation is involved in the etiology of autism spectrum disorder and other disorders which include social and cognitive deficits and/or increased anxiety.

Two communities, one highway and the fight for clean air: the role of political history in shaping community engagement and environmental health research translation.

BACKGROUND: This paper explores strategies to engage community stakeholders in efforts to address the effects of traffic-related air pollution (TRAP). Communities of color and low-income communities are disproportionately impacted by environmental threats including emissions generated by major roadways. METHODS: Qualitative instrumental case study design was employed to examine how community-level factors in two Massachusetts communities, the City of Somerville and Boston's Chinatown neighborhood, influence the translation of research into practice to address TRAP exposure. Guided by the Interactive Systems Framework (ISF), we drew on three data sources: key informant interviews, observations and document reviews. Thematic analysis was used. RESULTS: Findings indicate political history plays a significant role in shaping community action. In Somerville, community organizers worked with city and state officials, and embraced community development strategies to engage residents. In contrast, Chinatown community activists focused on immediate resident concerns including housing and resident displacement resulting in more opposition to local municipal leadership. CONCLUSIONS: The ISF was helpful in informing the team's thinking related to systems and structures needed to translate research to practice. However, although municipal stakeholders are increasingly sympathetic to and aware of the health impacts of TRAP, there was not a local legislative or regulatory precedent on how to move some of the proposed TRAP-related policies into practice. As such, we found that pairing the ISF with a community organizing framework may serve as a useful approach for examining the dynamic relationship between science, community engagement and environmental research translation. Social workers and public health professionals can advance TRAP exposure mitigation by exploring the political and social context of communities and working to bridge research and community action.

Bedding-generated particulate matter: implications for rodent studies.

OBJECTIVES: Rodents used in scientific research are typically housed in cages containing natural bedding materials. Despite extensive evidence of biological harm from inhaled particulate matter (PM), relatively little work has been performed to measure bedding-generated PM exposure in caged animals used in basic science research. Our objectives were to determine whether bedding-generated PM was present in significant concentrations in rodent cages and to identify the main factors affecting the accumulation and attenuation of bedding-generated PM inside cages. MATERIALS AND METHODS: We measured PM2.5 concentrations in cages containing common bedding materials (pine, aspen, paper, and corncob) with filter top isolator absent or present on the cages. PM2.5 concentrations were monitored with rats inside cages as well as during artificial manipulation of the bedding (designed to simulate rodent activity). RESULTS AND DISCUSSION: Upon rodent digging or mechanical/manual stirring, all four bedding materials produced significant increases in PM2.5 concentrations (as much as 100-200 µg/m3 PM2.5, 50- to 100-fold higher than during periods of no rodent activity), and concentrations in cages fitted with filter tops were an order of magnitude higher than in cages without filter tops. Elevated concentrations were sustained for longer durations in cages with filter tops (5-10 minutes) compared to cages with only bar lids (0-2 minutes). CONCLUSIONS: These results indicate that standard laboratory housing conditions can expose rodents to substantial levels of PM2.5. Bedding-generated PM has potential implications as an environmental agent in rodent studies.

Combining Measurements from Mobile Monitoring and a Reference Site To Develop Models of Ambient Ultrafine Particle Number Concentration at Residences.

Significant spatial and temporal variation in ultrafine particle (UFP; <100 nm in diameter) concentrations creates challenges in developing predictive models for epidemiological investigations. We compared the performance of land-use regression models built by combining mobile and stationary measurements (hybrid model) with a regression model built using mobile measurements only (mobile model) in Chelsea and Boston, MA (USA). In each study area, particle number concentration (PNC; a proxy for UFP) was measured at a stationary reference site and with a mobile laboratory driven along a fixed route during an ∼1-year monitoring period. In comparing PNC measured at 20 residences and PNC estimates from hybrid and mobile models, the hybrid model showed higher Pearson correlations of natural log-transformed PNC ( r = 0.73 vs 0.51 in Chelsea; r = 0.74 vs 0.47 in Boston) and lower root-mean-square error in Chelsea (0.61 vs 0.72) but no benefit in Boston (0.72 vs 0.71). All models overpredicted log-transformed PNC by 3-6% at residences, yet the hybrid model reduced the standard deviation of the residuals by 15% in Chelsea and 31% in Boston with better tracking of overnight decreases in PNC. Overall, the hybrid model considerably outperformed the mobile model and could offer reduced exposure error for UFP epidemiology.

Longitudinal associations of long-term exposure to ultrafine particles with blood pressure and systemic inflammation in Puerto Rican adults.

BACKGROUND: Few longitudinal studies have examined the association between ultrafine particulate matter (UFP, particles < 0.1 µm aerodynamic diameter) exposure and cardiovascular disease (CVD) risk factors. We used data from 791 adults participating in the longitudinal Boston Puerto Rican Health Study (Massachusetts, USA) between 2004 and 2015 to assess whether UFP exposure was associated with blood pressure and high sensitivity C-reactive protein (hsCRP, a biomarker of systemic inflammation). METHODS: Residential annual average UFP exposure (measured as particle number concentration, PNC) was assigned using a model accounting for spatial and temporal trends. We also adjusted PNC values for participants' inhalation rate to obtain the particle inhalation rate (PIR) as a secondary exposure measure. Multilevel linear models with a random intercept for each participant were used to examine the association of UFP with blood pressure and hsCRP. RESULTS: Overall, in adjusted models, an inter-quartile range increase in PNC was associated with increased hsCRP (ß = 6.8; 95% CI = - 0.3, 14.0%) but not with increased systolic blood pressure (ß = 0.96; 95% CI = - 0.33, 2.25 mmHg), pulse pressure (ß = 0.70; 95% CI = - 0.27, 1.67 mmHg), or diastolic blood pressure (ß = 0.55; 95% CI = - 0.20, 1.30 mmHg). There were generally stronger positive associations among women and never smokers. Among men, there were inverse associations of PNC with systolic blood pressure and pulse pressure. In contrast to the primary findings, an inter-quartile range increase in the PIR was positively associated with systolic blood pressure (ß = 1.03; 95% CI = 0.00, 2.06 mmHg) and diastolic blood pressure (ß = 1.01; 95% CI = 0.36, 1.66 mmHg), but not with pulse pressure or hsCRP. CONCLUSIONS: We observed that exposure to PNC was associated with increases in measures of CVD risk markers, especially among certain sub-populations. The exploratory PIR exposure metric should be further developed.

Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles.

Comparative evaluations are needed to assess the suitability of near-road air pollution models for traffic-related ultrafine particle number concentration (PNC). Our goal was to evaluate the ability of dispersion (CALINE4, AERMOD, R-LINE, and QUIC) and regression models to predict PNC in a residential neighborhood (Somerville) and an urban center (Chinatown) near highways in and near Boston, Massachusetts. PNC was measured in each area, and models were compared to each other and measurements for hot (>18 °C) and cold (<10 °C) hours with wind directions parallel to and perpendicular downwind from highways. In Somerville, correlation and error statistics were typically acceptable, and all models predicted concentration gradients extending ∼100 m from the highway. In contrast, in Chinatown, PNC trends differed among models, and predictions were poorly correlated with measurements likely due to effects of street canyons and nonhighway particle sources. Our results demonstrate the importance of selecting PNC models that align with study area characteristics (e.g., dominant sources and building geometry). We applied widely available models to typical urban study areas; therefore, our results should be generalizable to models of hourly averaged PNC in similar urban areas.

Comparisons of Traffic-Related Ultrafine Particle Number Concentrations Measured in Two Urban Areas by Central, Residential, and Mobile Monitoring.

Traffic-related ultrafine particles (UFP; <100 nanometers diameter) are ubiquitous in urban air. While studies have shown that UFP are toxic, epidemiological evidence of health effects, which is needed to inform risk assessment at the population scale, is limited due to challenges of accurately estimating UFP exposures. Epidemiologic studies often use empirical models to estimate UFP exposures; however, the monitoring strategies upon which the models are based have varied between studies. Our study compares particle number concentrations (PNC; a proxy for UFP) measured by three different monitoring approaches (central-site, short-term residential-site, and mobile on-road monitoring) in two study areas in metropolitan Boston (MA, USA). Our objectives were to quantify ambient PNC differences between the three monitoring platforms, compare the temporal patterns and the spatial heterogeneity of PNC between the monitoring platforms, and identify factors that affect correlations across the platforms. We collected >12,000 hours of measurements at the central sites, 1,000 hours of measurements at each of 20 residential sites in the two study areas, and >120 hours of mobile measurements over the course of ~1 year in each study area. Our results show differences between the monitoring strategies: mean one-minute PNC on-roads were higher (64,000 and 32,000 particles/cm3 in Boston and Chelsea, respectively) compared to central-site measurements (23,000 and 19,000 particles/cm3) and both were higher than at residences (14,000 and 15,000 particles/cm3). Temporal correlations and spatial heterogeneity also differed between the platforms. Temporal correlations were generally highest between central and residential sites, and lowest between central-site and on-road measurements. We observed the greatest spatial heterogeneity across monitoring platforms during the morning rush hours (06:00-09:00) and the lowest during the overnight hours (18:00-06:00). Longer averaging times (days and hours vs. minutes) increased temporal correlations (Pearson correlations were 0.69 and 0.60 vs. 0.39 in Boston; 0.71 and 0.61 vs. 0.45 in Chelsea) and reduced spatial heterogeneity (coefficients of divergence were 0.24 and 0.29 vs. 0.33 in Boston; 0.20 and 0.27 vs. 0.31 in Chelsea). Our results suggest that combining stationary and mobile monitoring may lead to improved characterization of UFP in urban areas and thereby lead to improved exposure assignment for epidemiology studies.

Lessons from in-home air filtration intervention trials to reduce urban ultrafine particle number concentrations.

BACKGROUND: Exposure to airborne ultrafine particle (UFP; <100 nm in aerodynamic diameter) is an emerging public health problem. Nevertheless, the benefit of using high efficiency particulate arrestance (HEPA) filtration to reduce UFP concentrations in homes is not yet clear. METHODS: We conducted a randomized crossover study of HEPA filtration without a washout period in 23 homes of low-income Puerto Ricans in Boston and Chelsea, MA (USA). Most participants were female, older adults who were overweight or obese. Particle number concentrations (PNC, a proxy for UFP) were measured indoors and outdoors at each home continuously for six weeks. Homes received both HEPA filtration and sham filtration for three weeks each in random order. RESULTS: Median PNC under HEPA filtration was 50-85% lower compared to sham filtration in most homes, but we found no benefit in terms of reduced inflammation; associations between hsCRP, IL-6, or TNFRII in blood samples and indoor PNC were inverse and not statistically significant. CONCLUSIONS: Limitations to our study design likely contributed to our findings. Limitations included carry-over effects, a population that may have been relatively unresponsive to UFP, reduction in PNC even during sham filtration that limited differences between HEPA and sham filtration, window opening by participants, and lack of fine-grained (room-specific) participant time-activity information. Our approach was similar to other recent HEPA intervention studies of particulate matter exposure and cardiovascular risk, suggesting that there is a need for better study designs.

Transferability and generalizability of regression models of ultrafine particles in urban neighborhoods in the Boston area.

Land use regression (LUR) models have been used to assess air pollutant exposure, but limited evidence exists on whether location-specific LUR models are applicable to other locations (transferability) or general models are applicable to smaller areas (generalizability). We tested transferability and generalizability of spatial-temporal LUR models of hourly particle number concentration (PNC) for Boston-area (MA, U.S.A.) urban neighborhoods near Interstate 93. Four neighborhood-specific regression models and one Boston-area model were developed from mobile monitoring measurements (34-46 days/neighborhood over one year each). Transferability was tested by applying each neighborhood-specific model to the other neighborhoods; generalizability was tested by applying the Boston-area model to each neighborhood. Both the transferability and generalizability of models were tested with and without neighborhood-specific calibration. Important PNC predictors (adjusted-R(2) = 0.24-0.43) included wind speed and direction, temperature, highway traffic volume, and distance from the highway edge. Direct model transferability was poor (R(2) < 0.17). Locally-calibrated transferred models (R(2) = 0.19-0.40) and the Boston-area model (adjusted-R(2) = 0.26, range: 0.13-0.30) performed similarly to neighborhood-specific models; however, some coefficients of locally calibrated transferred models were uninterpretable. Our results show that transferability of neighborhood-specific LUR models of hourly PNC was limited, but that a general model performed acceptably in multiple areas when calibrated with local data.

An hourly regression model for ultrafine particles in a near-highway urban area.

Estimating ultrafine particle number concentrations (PNC) near highways for exposure assessment in chronic health studies requires models capable of capturing PNC spatial and temporal variations over the course of a full year. The objectives of this work were to describe the relationship between near-highway PNC and potential predictors, and to build and validate hourly log-linear regression models. PNC was measured near Interstate 93 (I-93) in Somerville, MA using a mobile monitoring platform driven for 234 h on 43 days between August 2009 and September 2010. Compared to urban background, PNC levels were consistently elevated within 100-200 m of I-93, with gradients impacted by meteorological and traffic conditions. Temporal and spatial variables including wind speed and direction, temperature, highway traffic, and distance to I-93 and major roads contributed significantly to the full regression model. Cross-validated model R(2) values ranged from 0.38 to 0.47, with higher values achieved (0.43 to 0.53) when short-duration PNC spikes were removed. The model predicts highest PNC near major roads and on cold days with low wind speeds. The model allows estimation of hourly ambient PNC at 20-m resolution in a near-highway neighborhood.

Spatial and temporal differences in traffic-related air pollution in three urban neighborhoods near an interstate highway.

Relatively few studies have characterized differences in intra- and inter-neighborhood traffic-related air pollutant (TRAP) concentrations and distance-decay gradients in along an urban highway for the purposes of exposure assessment. The goal of this work was to determine the extent to which intra- and inter-neighborhood differences in TRAP concentrations can be explained by traffic and meteorology in three pairs of neighborhoods along Interstate 93 (I-93) in the metropolitan Boston area (USA). We measured distance-decay gradients of seven TRAPs (PNC, pPAH, NO, NOX, BC, CO, PM2.5) in near-highway (<400 m) and background areas (>1 km) in Somerville, Dorchester/South Boston, Chinatown and Malden to determine whether (1) spatial patterns in concentrations and inter-pollutant correlations differ between neighborhoods, and (2) variation within and between neighborhoods can be explained by traffic and meteorology. The neighborhoods ranged in area from 0.5 to 2.3 km2. Mobile monitoring was performed over the course of one year in each pair of neighborhoods (one pair of neighborhoods per year in three successive years; 35-47 days of monitoring in each neighborhood). Pollutant levels generally increased with highway proximity, consistent with I-93 being a major source of TRAP; however, the slope and extent of the distance-decay gradients varied by neighborhood as well as by pollutant, season and time of day. Correlations among pollutants differed between neighborhoods (e.g., ρ = 0.35-0.80 between PNC and NOX and ρ = 0.11-0.60 between PNC and BC) and were generally lower in Dorchester/South Boston than in the other neighborhoods. We found that the generalizability of near-road gradients and near-highway/urban background contrasts was limited for near-highway neighborhoods in a metropolitan area with substantial local street traffic. Our findings illustrate the importance of measuring gradients of multiple pollutants under different ambient conditions in individual near-highway neighborhoods for health studies involving inter-neighborhood comparisons.

Particle number emission factors for an urban highway tunnel.

Exposure to traffic-related air pollution has been linked to increased risks of cardiopulmonary disease, asthma, and reduced lung function. Ultrafine particles (UFP; aerodynamic diameter < 100 nm), one component of traffic exhaust, may contribute to these risks. This paper describes the development of UFP emission factors, an important input parameter for dispersion models used for exposure assessment. Measurements of particle number concentration (PNC), a proxy for UFP, were performed in the Central Artery Tunnel on Interstate-93 in Boston (MA, USA). The tunnel system consists of two, unidirectional bores, which each carry ~9 × 104 vehicles per day (diesel vehicles comprise 2-5% of the fleet in the southbound tunnel and 1-3% in the northbound tunnel). A tunnel was chosen for study because it provided an enclosed environment where the effe1cts of lateral and vertical dispersion by ambient air and photochemical reactions would be minimized. Data were collected using a mobile platform equipped with rapid-response instruments for measuring PNC (4-3000 nm) as well as NOx. Because Boston is located in a temperate region (latitude 42° N), we were interested in studying seasonal and diurnal differences in emission factors. To characterize seasonal differences, mobile monitoring was performed on 36 days spaced at 7-14 day intervals over one year (Sept. 2010-Sept. 2011); to characterize diurnal differences intensive mobile monitoring (n = 90 total trips through the tunnels) was performed over the course of two consecutive days in January 2012. All data collected during congested traffic conditions (~7% of total data set) were removed from the analysis. The median PNC inside the two tunnels for all trips during the 12-month campaign was 3-4-fold higher than on I-93 immediately outside the tunnel and 7-10-fold higher than on I-93 4 km from the tunnel. The median particle number emission factors (EFPN) (±median absolute deviation) for the southbound and northbound tunnels were 5.1 × 1014 (2.3 × 1014) and 1.4 × 1014 (4.2 × 1013) particles vehicle-1 km-1, respectively. EFPN values were ~2-fold higher in winter and spring (average ambient temperature at the time of monitoring = 6.9 °C) compared to summer and fall (12.9 °C), and ~2-fold higher in the morning (-7.9 °C) compared to the afternoon/ evening (-0.9 °C) on two consecutive winter days. Our results suggest that seasonal and diurnal variations in particulate emissions from highway vehicles may be important to consider in developing EFPN values.

A community participatory study of cardiovascular health and exposure to near-highway air pollution: study design and methods.

Current literature is insufficient to make causal inferences or establish dose-response relationships for traffic-related ultrafine particles (UFPs) and cardiovascular (CV) health. The Community Assessment of Freeway Exposure and Health (CAFEH) is a cross-sectional study of the relationship between UFP and biomarkers of CV risk. CAFEH uses a community-based participatory research framework that partners university researchers with community groups and residents. Our central hypothesis is that chronic exposure to UFP is associated with changes in biomarkers. The study enrolled more than 700 residents from three near-highway neighborhoods in the Boston metropolitan area in Massachusetts, USA. All participants completed an in-home questionnaire and a subset (440+) completed an additional supplemental questionnaire and provided biomarkers. Air pollution monitoring was conducted by a mobile laboratory equipped with fast-response instruments, at fixed sites, and inside the homes of selected study participants. We seek to develop improved estimates of UFP exposure by combining spatiotemporal models of ambient UFP with data on participant time-activity and housing characteristics. Exposure estimates will then be compared with biomarker levels to ascertain associations. This article describes our study design and methods and presents preliminary findings from east Somerville, one of the three study communities.

Long-term measurement study of urban environmental low frequency noise.

BACKGROUND: Environmental low frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked threat to population health. Environmental noise has historically been measured and regulated by A-weighted decibel (dBA) metrics, which more heavily weight frequencies between 2000 and 5000 Hz. Limited research has been conducted to measure and characterize the LFN components of urban environmental noise. OBJECTIVES: We characterized LFN noise at two urban sites in Greater Boston, Massachusetts (USA) using dBA and full spectrum noise measurements with aims to (1.) analyze spatio-temporal differences in the two datasets; (2.) compare and contrast LFN metrics with dBA noise metrics in the two sites; and (3.) assess meteorological covariate contributions to LFN in the dataset. METHODS: We measured A- and C-weighted, and flat, unweighted noise levels and 1/3-octave band continuously for 5 months using sound level meters sampling at f = 1 Hz and we recorded sound samples at 44.1 kHz. Our measurement sites were located in two urban, densely populated communities, burdened by close proximity to bus, rail, and aircraft routes. RESULTS: We found that (1.) LFN does not follow the same seasonal trends as A-weighted dBA loudness; there are spatial differences in LFN and its very low frequency noise components (VLFN) between two urban sites; (2.) VLFN and LFN are statistically significant drivers of LCeq (nearly independent of frequency) minus LAeq, (LCeq-LAeq) >10 dB, an accepted LFN metric; and (3.) LFN was minimally affected by high wind speeds at either Site. IMPACT STATEMENT: Environmental low-frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked risk to population health. We measured environmental noise across the full spectrum of frequencies continuously for five months at two urban sites located in Environmental Justice communities. We found that LFN did not follow the same seasonal trends as A-weighted (dBA) loudness, and we observed spatial differences in LFN and very low frequency noise (VLFN < 20 Hz) at the two sites. Not characterizing LFN and basing noise regulations only on A-weightings, a poor predictor of LFN, may expose populations to LFN levels of concern.